Analysis of application data
Brian
6/16/2021
Analysis of application data from new students
Import data
dataset <- read.xlsx("application_data.xlsx","Sheet1",header=TRUE)Inspect data structure
str(dataset)## 'data.frame': 250 obs. of 5 variables:
## $ Degree : chr "Bachelar" "Bachelar" "Ph. D." "Ph. D." ...
## $ Department : chr "Department of Civil Engineering" "Department of Civil Engineering" "Department of Civil Engineering" "Department of Civil Engineering" ...
## $ College : chr "College of Engineering" "College of Engineering" "College of Engineering" "College of Engineering" ...
## $ Nationality : chr "Swaziland" "Canada" "Pakistan" "Ethiopia" ...
## $ Admission.Status: chr "admitted" "admitted" "admitted" "admitted" ...Descriptive analysis
By nationality
dataset %>%
count(Nationality, sort=TRUE) %>%
mutate(Nationality=paste(Nationality,n,sep="\n")) %>%
treemap(index="Nationality",
vSize="n", title="Nationality"
)
dataset %>%
group_by(Nationality) %>%
summarise(count = n()) %>%
arrange(desc(count)) %>%
mutate( proportion = count / sum(count) * 100 )## # A tibble: 44 x 3
## Nationality count proportion
## <chr> <int> <dbl>
## 1 Indonesia 37 14.8
## 2 Vietnam 30 12
## 3 India 25 10
## 4 Pakistan 19 7.6
## 5 Malaysia 15 6
## 6 Japan 12 4.8
## 7 Thailand 12 4.8
## 8 Belize 8 3.2
## 9 Swaziland 8 3.2
## 10 Turkey 8 3.2
## 11 Mongolia 6 2.4
## 12 Nepal 6 2.4
## 13 Egypt 5 2
## 14 Ethiopia 5 2
## 15 Philippines 5 2
## 16 Haiti 4 1.6
## 17 Saint Vincent and the Grenadines 4 1.6
## 18 South Korea 4 1.6
## 19 Sri Lanka 4 1.6
## 20 France 3 1.2
## 21 Bangladesh 2 0.8
## 22 Guatemala 2 0.8
## 23 Paraguay 2 0.8
## 24 Saint Lucia 2 0.8
## 25 Somaliland 2 0.8
## 26 United Kingdom 2 0.8
## 27 Australia 1 0.4
## 28 Benin 1 0.4
## 29 Botswana 1 0.4
## 30 Cambodia 1 0.4
## 31 Canada 1 0.4
## 32 Fiji 1 0.4
## 33 Honduras 1 0.4
## 34 Hungary 1 0.4
## 35 Kiribati 1 0.4
## 36 Morocco 1 0.4
## 37 North Korea 1 0.4
## 38 Norway 1 0.4
## 39 Palau 1 0.4
## 40 Peru 1 0.4
## 41 Russia 1 0.4
## 42 Slovenia 1 0.4
## 43 Tanzania 1 0.4
## 44 United States 1 0.4By college
dataset %>%
count(College, sort=TRUE) %>%
mutate(College=str_remove(College,"College of ")) %>%
mutate(College=paste(College,n,sep="\n")) %>%
treemap(index="College",
vSize="n", title="College"
)
dataset %>%
group_by(College) %>%
summarise(count = n()) %>%
arrange(desc(count)) %>%
mutate( proportion = count / sum(count) * 100 )## # A tibble: 10 x 3
## College count proportion
## <chr> <int> <dbl>
## 1 College of Agriculture and Natural Resources 102 40.8
## 2 College of Engineering 48 19.2
## 3 College of Management 42 16.8
## 4 College of Life Science 23 9.2
## 5 College of Liberal Arts 17 6.8
## 6 College of Veterinary Medicine 7 2.8
## 7 College of Electrical Engineering and Computer Science 6 2.4
## 8 College of Law and Poitics 3 1.2
## 9 International College of Innovation and Industry Liaison 1 0.4
## 10 Multicollege 1 0.4By department
dataset %>%
count(Department, sort=TRUE) %>%
mutate(Department=str_remove(Department,"Department of ")) %>%
mutate(Department=paste(Department,n,sep="\n")) %>%
treemap(index="Department",
vSize="n", title="Department"
)
dataset %>%
group_by(Department) %>%
summarise(count = n()) %>%
arrange(desc(count)) %>%
mutate( proportion = count / sum(count) * 100 )## # A tibble: 49 x 3
## Department count proportion
## <chr> <int> <dbl>
## 1 International Bachelor Program of Agribusiness 27 10.8
## 2 International Master Program of Agriculture 26 10.4
## 3 Department of Marketing 20 8
## 4 Graduate Institute of Biomedical Engineering 14 5.6
## 5 Department of Mechanical Engineering 13 5.2
## 6 Department of Foreign Languages and Literatures 10 4
## 7 Department of Animal Science 9 3.6
## 8 Department of Business Administration 9 3.6
## 9 Department of Applied Economics 8 3.2
## 10 Department of Chemical Engineering 7 2.8
## 11 Department of Physics 7 2.8
## 12 Department of Life Sciences 6 2.4
## 13 Department of Plant Pathology 6 2.4
## 14 Graduate Institute of Technology and Management 6 2.4
## 15 Department of Electrical Engineering 5 2
## 16 Department of Veterinary Medicine 5 2
## 17 Department of Civil Engineering 4 1.6
## 18 Department of Enviromental Engineering 4 1.6
## 19 Department of Materials Science and Engineering 4 1.6
## 20 Department of Soil and Environmental Sciences 4 1.6
## 21 Master Program for Agricultural Economics and Marketing 4 1.6
## 22 Department of Applied Mathematics 3 1.2
## 23 Department of Chinese Literature 3 1.2
## 24 Department of Entomology 3 1.2
## 25 Department of Food Science and Biotechnology 3 1.2
## 26 Department of Horticulture 3 1.2
## 27 Department of Management Information Systems 3 1.2
## 28 Graduate Institute of Biotechnology 3 1.2
## 29 Graduate Institute of International Politics 3 1.2
## 30 Department of Accounting 2 0.8
## 31 Department of Agronomy 2 0.8
## 32 Department of Chemistry 2 0.8
## 33 Department of Forestry 2 0.8
## 34 Graduate Institute of Microbiology and Public Health 2 0.8
## 35 Graduate Institute of Precision Engineering 2 0.8
## 36 Institute of Molecular Biology 2 0.8
## 37 International PhD Program in Taiwan and Transcultural Studi~ 2 0.8
## 38 Department of Bio-Industrial Mechatronics Engineering 1 0.4
## 39 Department of Computer Science and Engineering 1 0.4
## 40 Department of Finance 1 0.4
## 41 Department of History 1 0.4
## 42 Department of Soil and Water Conservation 1 0.4
## 43 Graduate Institute of Biochemistry 1 0.4
## 44 Graduate Institute of Library and Information Science 1 0.4
## 45 Graduate Institute of Sports and Health Management 1 0.4
## 46 Graduate Institute of Statistics 1 0.4
## 47 Institute of Genomics and Bioinfomatics 1 0.4
## 48 Ph.D. Program in Tissue Engineering and Regenerative Medici~ 1 0.4
## 49 Tricontinental Master Program in Global Studies 1 0.4By degree
dataset %>%
count(Degree, sort=TRUE) %>%
mutate(Degree=paste(Degree,n,sep="\n")) %>%
treemap(index="Degree",
vSize="n", title="Degree"
)
dataset %>%
group_by(Degree) %>%
summarise(count = n()) %>%
arrange(desc(count)) %>%
mutate( proportion = count / sum(count) * 100 )## # A tibble: 3 x 3
## Degree count proportion
## <chr> <int> <dbl>
## 1 Master 103 41.2
## 2 Bachelar 75 30
## 3 Ph. D. 72 28.8Relation analysis
Relation between nationality and college
Relation between nationality and department
Relation between nationality and degree
